Improvement of Recovery in Segmentation-Based Parallel Compressive Sensing
This paper extends the recently introduced 1-D Kronecker-based Compressive Sensing (CS) recovery technique to 2-D signals and images. Traditionally large sensing matrices are used while compressing images using CS. CS when applied to individual columns of the image instead of the entire image during the sensing phase, leads to smaller sensing matrices and reduction in computational complexity. For achieving further reduction in computational complexity, the column vectors are further segmented into smaller length segments and CS is applied to each of the smaller length segments. This segmentation process reduces quality of the recovered signal. To enhance the quality of the recovered signal, the entire column vector is recovered using the Kronecker-based CS recovery technique. Results obtained from compression of slices of Magnetic Resonance (MR) images from NCIGT database show an improvement in signal quality, in terms of structural similarity and reconstruction error, when compared to the repeated recovery applied to each segment individually.
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|2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018|
|Organisation||Department of Systems and Computer Engineering|
Mitra, D. (Dipayan), Zanddizari, H. (Hadi), & Rajan, S. (2019). Improvement of Recovery in Segmentation-Based Parallel Compressive Sensing. In 2018 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2018 (pp. 141–146). doi:10.1109/ISSPIT.2018.8642662